{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a78f7f07-395d-4dfd-a468-8b858f166336",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "葡萄酒数据集如下：\n",
      "     label     a1    a2    a3    a4   a5    a6    a7    a8    a9    a10   a11  \\\n",
      "0        1  14.23  1.71  2.43  15.6  127  2.80  3.06  0.28  2.29   5.64  1.04   \n",
      "1        1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28   4.38  1.05   \n",
      "2        1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81   5.68  1.03   \n",
      "3        1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18   7.80  0.86   \n",
      "4        1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82   4.32  1.04   \n",
      "..     ...    ...   ...   ...   ...  ...   ...   ...   ...   ...    ...   ...   \n",
      "173      3  13.71  5.65  2.45  20.5   95  1.68  0.61  0.52  1.06   7.70  0.64   \n",
      "174      3  13.40  3.91  2.48  23.0  102  1.80  0.75  0.43  1.41   7.30  0.70   \n",
      "175      3  13.27  4.28  2.26  20.0  120  1.59  0.69  0.43  1.35  10.20  0.59   \n",
      "176      3  13.17  2.59  2.37  20.0  120  1.65  0.68  0.53  1.46   9.30  0.60   \n",
      "177      3  14.13  4.10  2.74  24.5   96  2.05  0.76  0.56  1.35   9.20  0.61   \n",
      "\n",
      "      a12   a13  \n",
      "0    3.92  1065  \n",
      "1    3.40  1050  \n",
      "2    3.17  1185  \n",
      "3    3.45  1480  \n",
      "4    2.93   735  \n",
      "..    ...   ...  \n",
      "173  1.74   740  \n",
      "174  1.56   750  \n",
      "175  1.56   835  \n",
      "176  1.62   840  \n",
      "177  1.60   560  \n",
      "\n",
      "[178 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names=['label','a1','a2','a3','a4','a5','a6','a7','a8','a9','a10','a11','a12','a13']\n",
    "dataset=pd.read_csv(\"wine.data\",names=names)\n",
    "print(\"葡萄酒数据集如下：\")\n",
    "print(dataset)\n",
    "data=dataset.iloc[range(0,178),range(1,14)]\n",
    "target=dataset.iloc[range(0,178),range(0,1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "41d499ae-73cf-4792-af91-02cd0043881c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a 1 中异常值:  []\n",
      "a 2 中异常值:  [5.8 5.51 5.65]\n",
      "a 3 中异常值:  [1.36 3.22 3.23]\n",
      "a 4 中异常值:  [10.6 30.0 28.5 28.5]\n",
      "a 5 中异常值:  [151.0 139.0 136.0 162.0]\n",
      "a 6 中异常值:  []\n",
      "a 7 中异常值:  []\n",
      "a 8 中异常值:  []\n",
      "a 9 中异常值:  [3.28 3.58]\n",
      "a 10 中异常值:  [10.8 13.0 11.75 10.68]\n",
      "a 11 中异常值:  [1.71]\n",
      "a 12 中异常值:  []\n",
      "a 13 中异常值:  []\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-v0_8-darkgrid')\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "data.plot(kind='box',subplots=True,layout=(3,5),sharex=False,sharey=False)\n",
    "p=data.boxplot(return_type='dict')\n",
    "for i in range(13):\n",
    "    y=p['fliers'][i].get_ydata()\n",
    "    print('a',i+1,'中异常值: ',y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddb9d7fd-82ef-4d47-bcde-119f4243f5b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
